{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65CU-n-JHhbY"
      },
      "source": [
        "# Clone repository"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_0vZ-OjHVNu",
        "outputId": "5253e994-98c1-4832-d7da-8a3965838526"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'vits'...\n",
            "remote: Enumerating objects: 121, done.\u001b[K\n",
            "remote: Counting objects: 100% (71/71), done.\u001b[K\n",
            "remote: Compressing objects: 100% (51/51), done.\u001b[K\n",
            "remote: Total 121 (delta 36), reused 20 (delta 20), pack-reused 50\u001b[K\n",
            "Receiving objects: 100% (121/121), 4.73 MiB | 4.22 MiB/s, done.\n",
            "Resolving deltas: 100% (45/45), done.\n",
            "/content/vits\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/AlexandaJerry/vits.git\n",
        "%cd vits"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!/opt/bin/nvidia-smi"
      ],
      "metadata": {
        "id": "smGF80ufrZxu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0389abe-8b66-44c9-a710-291b020c9f53"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Aug 11 03:38:05 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "6vOAlBTavYsc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c9d802fb-78c2-43e2-830f-0daacb73d819"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting Cython==0.29.21\n",
            "  Downloading Cython-0.29.21-cp37-cp37m-manylinux1_x86_64.whl (2.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.0 MB 4.3 MB/s \n",
            "\u001b[?25hCollecting librosa==0.8.0\n",
            "  Downloading librosa-0.8.0.tar.gz (183 kB)\n",
            "\u001b[K     |████████████████████████████████| 183 kB 82.9 MB/s \n",
            "\u001b[?25hCollecting matplotlib==3.3.1\n",
            "  Downloading matplotlib-3.3.1-cp37-cp37m-manylinux1_x86_64.whl (11.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 11.6 MB 65.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy==1.21.6 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (1.21.6)\n",
            "Collecting phonemizer==2.2.1\n",
            "  Downloading phonemizer-2.2.1-py3-none-any.whl (49 kB)\n",
            "\u001b[K     |████████████████████████████████| 49 kB 7.0 MB/s \n",
            "\u001b[?25hCollecting scipy==1.5.2\n",
            "  Downloading scipy-1.5.2-cp37-cp37m-manylinux1_x86_64.whl (25.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 25.9 MB 64 kB/s \n",
            "\u001b[?25hCollecting Unidecode==1.1.1\n",
            "  Downloading Unidecode-1.1.1-py2.py3-none-any.whl (238 kB)\n",
            "\u001b[K     |████████████████████████████████| 238 kB 100.3 MB/s \n",
            "\u001b[?25hCollecting pyopenjtalk==0.2.0\n",
            "  Downloading pyopenjtalk-0.2.0.tar.gz (1.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5 MB 60.2 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (2.1.9)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (1.1.0)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (4.4.2)\n",
            "Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (0.3.1)\n",
            "Requirement already satisfied: numba>=0.43.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (0.56.0)\n",
            "Requirement already satisfied: soundfile>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (0.10.3.post1)\n",
            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (1.6.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (1.4.4)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (0.11.0)\n",
            "Requirement already satisfied: certifi>=2020.06.20 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (2022.6.15)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (2.8.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (7.1.2)\n",
            "Requirement already satisfied: attrs>=18.1 in /usr/local/lib/python3.7/dist-packages (from phonemizer==2.2.1->-r requirements.txt (line 5)) (22.1.0)\n",
            "Collecting segments\n",
            "  Downloading segments-2.2.1-py2.py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pyopenjtalk==0.2.0->-r requirements.txt (line 8)) (4.64.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from pyopenjtalk==0.2.0->-r requirements.txt (line 8)) (1.15.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib==3.3.1->-r requirements.txt (line 3)) (4.1.1)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa==0.8.0->-r requirements.txt (line 2)) (0.39.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa==0.8.0->-r requirements.txt (line 2)) (57.4.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa==0.8.0->-r requirements.txt (line 2)) (4.12.0)\n",
            "Requirement already satisfied: appdirs>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (1.4.4)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (2.23.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (21.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (1.24.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa==0.8.0->-r requirements.txt (line 2)) (3.1.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from soundfile>=0.9.0->librosa==0.8.0->-r requirements.txt (line 2)) (1.15.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0->soundfile>=0.9.0->librosa==0.8.0->-r requirements.txt (line 2)) (2.21)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.43.0->librosa==0.8.0->-r requirements.txt (line 2)) (3.8.1)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (2022.6.2)\n",
            "Collecting csvw>=1.5.6\n",
            "  Downloading csvw-3.1.1-py2.py3-none-any.whl (56 kB)\n",
            "\u001b[K     |████████████████████████████████| 56 kB 4.8 MB/s \n",
            "\u001b[?25hCollecting clldutils>=1.7.3\n",
            "  Downloading clldutils-3.12.0-py2.py3-none-any.whl (197 kB)\n",
            "\u001b[K     |████████████████████████████████| 197 kB 84.8 MB/s \n",
            "\u001b[?25hCollecting colorlog\n",
            "  Downloading colorlog-6.6.0-py2.py3-none-any.whl (11 kB)\n",
            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from clldutils>=1.7.3->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (0.8.10)\n",
            "Collecting language-tags\n",
            "  Downloading language_tags-1.1.0-py2.py3-none-any.whl (210 kB)\n",
            "\u001b[K     |████████████████████████████████| 210 kB 94.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: jsonschema in /usr/local/lib/python3.7/dist-packages (from csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (4.3.3)\n",
            "Collecting isodate\n",
            "  Downloading isodate-0.6.1-py2.py3-none-any.whl (41 kB)\n",
            "\u001b[K     |████████████████████████████████| 41 kB 688 kB/s \n",
            "\u001b[?25hCollecting rfc3986<2\n",
            "  Downloading rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)\n",
            "Requirement already satisfied: uritemplate>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (3.0.1)\n",
            "Requirement already satisfied: babel in /usr/local/lib/python3.7/dist-packages (from csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (2.10.3)\n",
            "Collecting rdflib\n",
            "  Downloading rdflib-6.2.0-py3-none-any.whl (500 kB)\n",
            "\u001b[K     |████████████████████████████████| 500 kB 71.5 MB/s \n",
            "\u001b[?25hCollecting colorama\n",
            "  Downloading colorama-0.4.5-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: pytz>=2015.7 in /usr/local/lib/python3.7/dist-packages (from babel->csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (2022.1)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema->csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (0.18.1)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema->csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (5.9.0)\n",
            "Building wheels for collected packages: librosa, pyopenjtalk\n",
            "  Building wheel for librosa (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for librosa: filename=librosa-0.8.0-py3-none-any.whl size=201396 sha256=8a9c35af63cf3d5d3f035e4254a1e9210f42710d53ff64373300c1b6fe0fad32\n",
            "  Stored in directory: /root/.cache/pip/wheels/de/1e/aa/d91797ae7e1ce11853ee100bee9d1781ae9d750e7458c95afb\n",
            "  Building wheel for pyopenjtalk (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyopenjtalk: filename=pyopenjtalk-0.2.0-cp37-cp37m-linux_x86_64.whl size=4434530 sha256=4d697235e36ec52c35b525869a679849da56f1bcb5b28c762b46644781e6b1a9\n",
            "  Stored in directory: /root/.cache/pip/wheels/10/56/0e/435dc1aec0d8614a489abfc51da4fd54ff6e8b33bf978f2081\n",
            "Successfully built librosa pyopenjtalk\n",
            "Installing collected packages: isodate, rfc3986, rdflib, language-tags, colorama, csvw, colorlog, scipy, clldutils, segments, Cython, Unidecode, pyopenjtalk, phonemizer, matplotlib, librosa\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.7.3\n",
            "    Uninstalling scipy-1.7.3:\n",
            "      Successfully uninstalled scipy-1.7.3\n",
            "  Attempting uninstall: Cython\n",
            "    Found existing installation: Cython 0.29.32\n",
            "    Uninstalling Cython-0.29.32:\n",
            "      Successfully uninstalled Cython-0.29.32\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Attempting uninstall: librosa\n",
            "    Found existing installation: librosa 0.8.1\n",
            "    Uninstalling librosa-0.8.1:\n",
            "      Successfully uninstalled librosa-0.8.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pymc3 3.11.5 requires scipy<1.8.0,>=1.7.3, but you have scipy 1.5.2 which is incompatible.\u001b[0m\n",
            "Successfully installed Cython-0.29.21 Unidecode-1.1.1 clldutils-3.12.0 colorama-0.4.5 colorlog-6.6.0 csvw-3.1.1 isodate-0.6.1 language-tags-1.1.0 librosa-0.8.0 matplotlib-3.3.1 phonemizer-2.2.1 pyopenjtalk-0.2.0 rdflib-6.2.0 rfc3986-1.5.0 scipy-1.5.2 segments-2.2.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  espeak-data libespeak1 libportaudio2 libsonic0\n",
            "The following NEW packages will be installed:\n",
            "  espeak espeak-data libespeak1 libportaudio2 libsonic0\n",
            "0 upgraded, 5 newly installed, 0 to remove and 19 not upgraded.\n",
            "Need to get 1,219 kB of archives.\n",
            "After this operation, 3,031 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libportaudio2 amd64 19.6.0-1 [64.6 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsonic0 amd64 0.2.0-6 [13.4 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 espeak-data amd64 1.48.04+dfsg-5 [934 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libespeak1 amd64 1.48.04+dfsg-5 [145 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 espeak amd64 1.48.04+dfsg-5 [61.6 kB]\n",
            "Fetched 1,219 kB in 2s (489 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 5.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package libportaudio2:amd64.\n",
            "(Reading database ... 155680 files and directories currently installed.)\n",
            "Preparing to unpack .../libportaudio2_19.6.0-1_amd64.deb ...\n",
            "Unpacking libportaudio2:amd64 (19.6.0-1) ...\n",
            "Selecting previously unselected package libsonic0:amd64.\n",
            "Preparing to unpack .../libsonic0_0.2.0-6_amd64.deb ...\n",
            "Unpacking libsonic0:amd64 (0.2.0-6) ...\n",
            "Selecting previously unselected package espeak-data:amd64.\n",
            "Preparing to unpack .../espeak-data_1.48.04+dfsg-5_amd64.deb ...\n",
            "Unpacking espeak-data:amd64 (1.48.04+dfsg-5) ...\n",
            "Selecting previously unselected package libespeak1:amd64.\n",
            "Preparing to unpack .../libespeak1_1.48.04+dfsg-5_amd64.deb ...\n",
            "Unpacking libespeak1:amd64 (1.48.04+dfsg-5) ...\n",
            "Selecting previously unselected package espeak.\n",
            "Preparing to unpack .../espeak_1.48.04+dfsg-5_amd64.deb ...\n",
            "Unpacking espeak (1.48.04+dfsg-5) ...\n",
            "Setting up libportaudio2:amd64 (19.6.0-1) ...\n",
            "Setting up espeak-data:amd64 (1.48.04+dfsg-5) ...\n",
            "Setting up libsonic0:amd64 (0.2.0-6) ...\n",
            "Setting up libespeak1:amd64 (1.48.04+dfsg-5) ...\n",
            "Setting up espeak (1.48.04+dfsg-5) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.5) ...\n"
          ]
        }
      ],
      "source": [
        "!pip install -r requirements.txt\n",
        "!sudo apt-get install espeak -y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0iLn2JxKYhl"
      },
      "source": [
        "# Mount Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOgjdsQgKTfD",
        "outputId": "017de1b4-c6c6-42d4-93be-337b303fbb55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZ9maSoUmHaS"
      },
      "source": [
        "# Unpack dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "N3a-FsHghwXS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f48efc4-28c0-404d-8091-48c50df90c69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "p7zip-full is already the newest version (16.02+dfsg-6).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 19 not upgraded.\n",
            "\n",
            "7-Zip [64] 16.02 : Copyright (c) 1999-2016 Igor Pavlov : 2016-05-21\n",
            "p7zip Version 16.02 (locale=en_US.UTF-8,Utf16=on,HugeFiles=on,64 bits,4 CPUs Intel(R) Xeon(R) CPU @ 2.30GHz (306F0),ASM,AES-NI)\n",
            "\n",
            "Scanning the drive for archives:\n",
            "  0M Scan /content/drive/MyDrive/\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b1 file, 306244184 bytes (293 MiB)\n",
            "\n",
            "Extracting archive: /content/drive/MyDrive/可塑性记忆艾拉-训练集.zip\n",
            "--\n",
            "Path = /content/drive/MyDrive/可塑性记忆艾拉-训练集.zip\n",
            "Type = zip\n",
            "Physical Size = 306244184\n",
            "\n",
            "  0%\b\b\b\b    \b\b\b\b  3% 75 - isla/pm01_05_isl0036.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b  8% 155 - isla/pm1-01_isl0014.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 13% 236 - isla/pm1-04_isl0047.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 17% 320 - isla/pm1-08_isl0010.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 22% 407 - isla/pm1-15_isl0009.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 27% 510 - isla/pm2-05_isl0020.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 30% 591 - isla/pm2-08_isl0033.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 35% 681 - isla/pm2-13_isl0028.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 39% 749 - isla/pm2-16_isl0005.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 43% 825 - isla/pm2-19_isl0037.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 46% 905 - isla/pm2-22_isl0043.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 51% 993 - isla/pm3-01_isl0019.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                  \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 55% 1081 - isla/pm3-03_isl0048.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 59% 1156\b\b\b\b\b\b\b\b\b         \b\b\b\b\b\b\b\b\b 63% 1240 - isla/pm3-10_isl0025.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 66% 1326 - isla/pm3-14_isl0013.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 70% 1408 - isla/pm3-16_isl0051.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 74% 1493 - isla/pm3-20_isl0037.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 78% 1585 - isla/pm3-24_isl0015.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 82% 1661 - isla/pm5-03_isl0042.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 86% 1728 - isla/pm_a02_02_isl0002.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                      \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 91% 1787 - isla/pm_a02_02_isl0106.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                      \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 95% 1852 - isla/pm_e01_02_isl0016.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                      \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 99% 1935 - isla/pm_s01_05_isl0001.wav\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                      \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEverything is Ok\n",
            "\n",
            "Folders: 1\n",
            "Files: 1952\n",
            "Size:       368584852\n",
            "Compressed: 306244184\n"
          ]
        }
      ],
      "source": [
        "!sudo apt-get install p7zip-full\n",
        "!7z x /content/drive/MyDrive/可塑性记忆艾拉-训练集.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LY9d2hgjmYUF"
      },
      "source": [
        "# Alignment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LOsV22D8IUTS",
        "outputId": "c70b6578-e5be-43b9-90d8-eb2b606d6710"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/vits/monotonic_align\n",
            "Compiling core.pyx because it changed.\n",
            "[1/1] Cythonizing core.pyx\n",
            "/usr/local/lib/python3.7/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/vits/monotonic_align/core.pyx\n",
            "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
            "running build_ext\n",
            "building 'monotonic_align.core' extension\n",
            "creating build\n",
            "creating build/temp.linux-x86_64-3.7\n",
            "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.7/dist-packages/numpy/core/include -I/usr/include/python3.7m -c core.c -o build/temp.linux-x86_64-3.7/core.o\n",
            "creating /content/vits/monotonic_align/monotonic_align\n",
            "x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.7/core.o -o /content/vits/monotonic_align/monotonic_align/core.cpython-37m-x86_64-linux-gnu.so\n",
            "/content/vits\n"
          ]
        }
      ],
      "source": [
        "%cd monotonic_align\n",
        "!python setup.py build_ext --inplace\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5feIfJWdRNRB",
        "outputId": "60827076-d5ab-4cbe-ab18-537f3167e2f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/vits\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "path = \"/content/vits\"\n",
        "os.chdir(path)\n",
        "print(os.getcwd())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXYOrkJmRuSh",
        "outputId": "aad6bad4-7707-4bf7-8f7f-b858e7870449"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "START: /content/vits/filelists/train_filelist.txt\n",
            "START: /content/vits/filelists/val_filelist.txt\n"
          ]
        }
      ],
      "source": [
        "!python preprocess.py --text_index 1 --text_cleaners japanese_cleaners --filelists /content/vits/filelists/train_filelist.txt /content/vits/filelists/val_filelist.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjIAR_UsmPEz"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrV5GulH3us_",
        "outputId": "89fe46b2-b4da-4d06-9225-b2d8f7f6cca5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] {'train': {'log_interval': 200, 'eval_interval': 1000, 'seed': 1234, 'epochs': 200, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 24, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'filelists/train_filelist.txt.cleaned', 'validation_files': 'filelists/val_filelist.txt.cleaned', 'text_cleaners': ['japanese_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 0, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False}, 'model_dir': './logs/isla_base'}\n",
            "[WARNING] git hash values are different. fa259503(saved) != 9aeb60e9(current)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "./logs/isla_base/G_10000.pth\n",
            "[INFO] Loaded checkpoint './logs/isla_base/G_10000.pth' (iteration 127)\n",
            "./logs/isla_base/D_10000.pth\n",
            "[INFO] Loaded checkpoint './logs/isla_base/D_10000.pth' (iteration 127)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at  ../aten/src/ATen/EmptyTensor.cpp:31.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py:175: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed.  This is not an error, but may impair performance.\n",
            "grad.sizes() = [1, 9, 96], strides() = [19680, 96, 1]\n",
            "bucket_view.sizes() = [1, 9, 96], strides() = [864, 96, 1] (Triggered internally at  ../torch/csrc/distributed/c10d/reducer.cpp:312.)\n",
            "  allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n",
            "[INFO] Train Epoch: 127 [58%]\n",
            "[INFO] [2.7087249755859375, 1.8435219526290894, 3.053363561630249, 20.586633682250977, 1.6831552982330322, 1.3081979751586914, 10000, 0.00019684987340346216]\n",
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 127 to ./logs/isla_base/G_10000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 127 to ./logs/isla_base/D_10000.pth\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 127\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 128\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 129\n",
            "[INFO] Train Epoch: 130 [11%]\n",
            "[INFO] [2.690260887145996, 2.213076591491699, 3.6719186305999756, 22.936397552490234, 1.7311540842056274, 1.0785740613937378, 10200, 0.00019677606392788917]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 130\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 131\n",
            "[INFO] Train Epoch: 132 [65%]\n",
            "[INFO] [2.7264342308044434, 2.1505484580993652, 3.427931785583496, 20.655485153198242, 1.7136359214782715, 1.3893734216690063, 10400, 0.00019672687298653317]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 132\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 133\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 134\n",
            "[INFO] Train Epoch: 135 [18%]\n",
            "[INFO] [2.640322208404541, 2.150308847427368, 3.4743993282318115, 20.645612716674805, 1.6320246458053589, 1.1710439920425415, 10600, 0.00019665310963035113]\n"
          ]
        }
      ],
      "source": [
        "!python train.py -c configs/isla_base.json -m isla_base"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "S8gpGR7HUx8w"
      },
      "outputs": [],
      "source": [
        "#重新启动代码程序后再进行inference\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import IPython.display as ipd\n",
        "\n",
        "import os\n",
        "import json\n",
        "import math\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "import commons\n",
        "import utils\n",
        "from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate\n",
        "from models import SynthesizerTrn\n",
        "from text.symbols import symbols\n",
        "from text import text_to_sequence\n",
        "\n",
        "from scipy.io.wavfile import write\n",
        "\n",
        "\n",
        "def get_text(text, hps):\n",
        "    text_norm = text_to_sequence(text, hps.data.text_cleaners)\n",
        "    if hps.data.add_blank:\n",
        "        text_norm = commons.intersperse(text_norm, 0)\n",
        "    text_norm = torch.LongTensor(text_norm)\n",
        "    return text_norm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hps = utils.get_hparams_from_file(\"/content/vits/configs/isla_base.json\")"
      ],
      "metadata": {
        "id": "o3OCVb09sM3D"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "O3K5fYBnJSfT"
      },
      "outputs": [],
      "source": [
        "net_g = SynthesizerTrn(\n",
        "    len(symbols),\n",
        "    hps.data.filter_length // 2 + 1,\n",
        "    hps.train.segment_size // hps.data.hop_length,\n",
        "    **hps.model).cuda()\n",
        "_ = net_g.eval()\n",
        "\n",
        "_ = utils.load_checkpoint(\"/content/vits/logs/isla_base/G_10000.pth\", net_g, None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Q1MxZNQudiPa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "5c22459e-6ab3-44e9-b79c-2bf708e6ba8c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "text/html": [
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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "stn_tst = get_text(\"ビエザイゼリ,ーファーディエン\", hps)\n",
        "with torch.no_grad():\n",
        "    x_tst = stn_tst.cuda().unsqueeze(0)\n",
        "    x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()\n",
        "    audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()\n",
        "ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#把checkpoint存入google drive\n",
        "!cp -r /content/vits/logs/ /content/drive/MyDrive/isla_base"
      ],
      "metadata": {
        "id": "5Qx691T7K2W8"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#把google drive的checkpoint恢复到log\n",
        "!cp -r /content/drive/MyDrive/isla_base /content/vits/logs/"
      ],
      "metadata": {
        "id": "oqcCoW97JUPY"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "“vits.ipynb”的副本",
      "provenance": [],
      "machine_shape": "hm"
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}